Point-cloud ML models classify charm- and bottom-origin electrons at ~80% purity for 40% efficiency, outperforming a BDT baseline, with performance limited by intrinsic decay similarity.
Kasieczka et al., The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics , Rept
4 Pith papers cite this work. Polarity classification is still indexing.
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QUIVER augments classical ML models with a quantum Fisher view from VQCs to improve performance on QM9 molecular properties and JetClass jet flavor prediction.
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
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Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.